Diagnosis of Tomato Plant Diseases Using Pre-trained Architectures and A Proposed Convolutional Neural Network Model

Abstract

Tomatoes are of the most important vegetables in the world. Presence of diseases and pests in the growing area significantly affect the choice of variety in tomato. The aim of this study is to diagnose tomato plant diseases faster and with higher degrees of accuracy. For this purpose, deep learning was used to diagnose some diseases in tomatoes, including bacterial spot, early blight, leaf mold, septoria leaf spot, target spot, mosaic virus, and yellow leaf curl virus were analyzed CNN models. A CNN model with a 2D convolutional three layers, one flatten layer approach and several Keras models, including DenseNet201, InceptionResNetV2, MobileNet, Visual Geometry Group 16 architectures were proposed. The experimental results showed that the accuracy scores were 99.82%, 92.12%, 92.75%, 91.50% and 84.12% training accuracy, respectively. The proposed CNN model provided the opportunity for rapid diagnosis for approximately 14.9 minutes. The results obtained in this study can be used in robotic spraying and harvesting operations.

Keywords

• Automated diagnose
• convolutional neural network
• deep learning
• tomato

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